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Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering
BACKGROUD: Taking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4021249/ https://www.ncbi.nlm.nih.gov/pubmed/24717145 http://dx.doi.org/10.1186/1471-2105-15-102 |
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author | Guo, Xuan Meng, Yu Yu, Ning Pan, Yi |
author_facet | Guo, Xuan Meng, Yu Yu, Ning Pan, Yi |
author_sort | Guo, Xuan |
collection | PubMed |
description | BACKGROUD: Taking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. RESULTS: In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. CONCLUSIONS: Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS. |
format | Online Article Text |
id | pubmed-4021249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40212492014-05-28 Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering Guo, Xuan Meng, Yu Yu, Ning Pan, Yi BMC Bioinformatics Research Article BACKGROUD: Taking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. RESULTS: In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. CONCLUSIONS: Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS. BioMed Central 2014-04-10 /pmc/articles/PMC4021249/ /pubmed/24717145 http://dx.doi.org/10.1186/1471-2105-15-102 Text en Copyright © 2014 Guo et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedicationwaiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwisestated. |
spellingShingle | Research Article Guo, Xuan Meng, Yu Yu, Ning Pan, Yi Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering |
title | Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering |
title_full | Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering |
title_fullStr | Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering |
title_full_unstemmed | Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering |
title_short | Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering |
title_sort | cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4021249/ https://www.ncbi.nlm.nih.gov/pubmed/24717145 http://dx.doi.org/10.1186/1471-2105-15-102 |
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